The past few years have firmly established the importance of Big Data in the global business environment. 2017 looks to be the the year of greater Apache™ Hadoop implementation (both in terms of its open source development and more available commercial options) at the enterprise level, as Enterprise Information Management (EIM) continues to need more improved Big Data solutions.
The earlier (and continuing) trends of Data Warehouse modernization, Hadoop project-level adoption, and use of Data Lakes will likely continue forward at a greater pace. Between Versions 1 and 2, Hadoop has evolved from a primarily batch-oriented processor to a powerful, real-time data cruncher that can handle enterprise grade Big Data applications as well as more traditional, legacy datasets.
Today, Hadoop can deliver a data processing infrastructure that can accommodate large and complex business applications. With Big Data at the core of the processing model, typical business systems running on Hadoop include three distinct layers: the infrastructure layer, the data layer, and the analytics layer. Thus, commercial platform vendors such as MapR or Cloudera may find it easy to position Hadoop architecture as an omni-utility platform meeting most enterprise needs.
The Data Explosion in Modern Enterprises
The Forbes blog post titled 5 Reasons Hadoop Is Ready for Enterprise Prime Time explains how the data explosion has forced organizations to scale up their business applications through third-party, managed services without making large investments. In the managed service scenario, businesses do not have to worry about infrastructure, in-house Data Centers, or expert manpower – thus devoting the entire time and effort to speed of delivery.
The latest “icing on the cake” is the steady supply of open source solutions for Hadoop, which extend the power and capability of this unique data platform by several times. For supply chain systems, the story is a little different. The article What is Hadoop and What Does It Mean for Supply Chain Management argues that as the basis of supply chain, risk-assessment applications is vast troves of “unstructured data,” Hadoop with MapReduce and HDFS make a formidable combination for risk assessments and mitigation in supply chain programs.
Hadoop for Enterprise Information Management
Business datasets have gone beyond databases to web trails, GPS data, sensor data, and social data. The new “data environment” requires advanced technologies and tools to take advantage of vast amounts of multi-structured data, which can yield profitable intelligence and sights if processed with the right tools. The article also stresses that the huge data volumes have made it necessary to find cost-friendly technological solutions for storing and processing such data. Hadoop is a wonderful solution for Big-Data enabled technologies for delivering real benefits to business users.
The Seed Analytics Group explores the Big Data Challenges for EIM, where Big Data Analytics proves to be the core differentiator for success among stiff competition. Companies like LinkedIn have leveraged Big Data Analytics to move ahead of competition. The interesting observation mad here is that many leading software vendors have embraced Hadoop as their preferred platform for Big Data applications.
Globally, businesses are encouraged to start planning for Big Data on Hadoop, and Big Data Analytics, if they have not done it already. Here, the enterprise data framework has been clearly defined in four consecutive steps of: Data Acquisition, Data Cleansing, Data Processing, and Intelligence Gathering. An industry whitepaper titled Evolving Role of the Enterprise Data Warehouse in the Era of Big Data Analytics attempts to explain that Big Data technologies need to be adapted in the traditional Enterprise Information Management model.
The Database Trends and Applications magazine reports in Trend-Setting Products in Data and Information Management for 2017, that in recent times, the Cloud has emerged as a top data storage platform among organizations. Most of the organizations who participated in this 2016 survey conducted by DBTA Magazine have more than 100TB data.
Big Data on Hadoop in Many Flavors
The most popular open source version of Hadoop from Apache requires advanced technical skills, while subscribing to Hadoop-as-a-Service takes the maintenance burden off the client’s shoulders. HP has partnered with HortonWorks to drive a solid technical alliance between Hadoop and its own Big Data technologies.
On the other side of this broad spectrum, IBM offers both on-premise and hosted version Hadoop in the Cloud. As of now, many organizations who want to manage multi-structured, Big Data are likely relying on Hadoop to deliver the desirable results. The real challenge lies in selecting the appropriate analytics solution for Hadoop databases and their in-house applications.
Data Lakes: The Unique Hadoop Repository
The Data Lake has the capability to ingest raw data in diverse formats and can easily scale up to Petabytes. The biggest advantage of storing raw data in Data Lakes is that the data can be repeatedly repurposed with changing business needs and requirements. This allows data to be retained in the most flexible format for any new application.
Building Big Data Use Cases on Hadoop
An effective way to build the Hadoop infrastructure is through Big Data use cases. In order to build the best use case, an organization first needs manpower – a team of able Data Architects and Data Scientists who can visualize and build solutions from available data. Along with these experts, organizations also need Data Analysts and Business Intelligence experts to extract insights from the data. In an ideal situation, it is a multi-effort exercise requiring a wide variety of skills and experience.
The article titled Data Management Trends to Watch in 2017 suggests that the massive cost advantage of Hadoop storage facilities makes it the preferred choice for data storage in modern enterprises. The immense power of a Data Lake to retain data in its raw format makes it possible to repeatedly utilize that data for disparate applications.
Gartner published a helpful infographic to aid in understanding why Hadoop can deliver most of the data demands made by an Enterprise Information Management system, which requires a suitable integration of domains, road maps, processes, workflows driving desirable outcomes with full attention to data governance.
This graphic also attempts to describe the role of a Chief Data Officer, who can ideally lead the Data Governance and Data Stewardship efforts in large enterprise information networks.
Into the Future
As enterprise data volumes continue to rise in strategic importance, the traditional Enterprise Data Warehouse will continue to evolve into larger and more complex Data Architectures. From top executives to shop floor mangers, every business user will likely begin to utilize Big Data applications for reviewing, analyzing, and reporting mission-critical information during daily business operations.
Additionally, as advanced technologies like Machine Learning and Deep Learning get included in enterprise Big Data applications for predictive modeling, targeting customers, product pricing or recommendations, an open-source platform like Hadoop may be the perfect answer to cost-efficient Enterprise Information Management solutions. These trends will continue throughout 2017 (and beyond) and will also be strengthened by the SQL-ization of Hadoop and along with the growth of the Internet of Things (IoT).